UCP agentic-commerce proxy for Estroni: merino & low-tox women's activewear catalogue.
Scored across 1 tool
With only one tool, there is no possibility of confusion between tools. The tool has a clear, singular purpose of searching policies and FAQs.
The single tool name follows a clear verb_noun convention, making it consistent and predictable. No mixed styles or irregular patterns exist.
A single tool for a storefront is far too few to cover the expected functionality such as product browsing, cart management, order placement, or account features. This is an extreme mismatch between tool count and the server's apparent scope.
The tool surface is severely incomplete, covering only policy and FAQ lookups. Essential storefront operations like product search, order tracking, and customer support are entirely missing, leaving agents with dead ends for most tasks.
Used to get facts about the stores policies, products, or services. Some examples of questions you can ask are:
What is your return policy?
What is your shipping policy?
What is your phone number?
What are your hours of operation?"
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | A natural language query. | |
| context | No | Additional information about the request such as user demographics, mood, location, or other relevant details that could help in tailoring the response appropriately. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the burden of behavioral disclosure. It states the tool is used to 'get facts,' implying a read-only search, but offers no detail on output format, limitations, or behavior when no answer is found. Basic but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a single opening sentence followed by a short bulleted list of examples. Every element earns its place, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple FAQ search tool with only two flat parameters and no output schema, the description provides adequate usage examples and scope. It doesn't describe return type, but the tool's behavior is straightforward enough for an agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for both parameters ('query' and 'context'). The description does not add parameter-level detail beyond what the schema already states, so it provides no additional semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('get facts') and names the resource ('stores policies, products, or services'), making the general purpose clear. However, including 'products' creates ambiguity with sibling tools like get_product_details and search_catalog, so it doesn't fully distinguish from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clear context is provided via concrete examples of acceptable questions (return policy, shipping policy, phone number, hours). It doesn't explicitly state when not to use the tool or mention alternatives, but the examples imply this is for policy and FAQ queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_cartget_product_detailssearch_catalogupdate_cartget_cart1 field changedInput schema / $schemaAdded value: +"https://json-schema.org/draft/2020-12/schema"get_product_details1 field changedInput schema / $schemaAdded value: +"https://json-schema.org/draft/2020-12/schema"search_catalog1 field changedInput schema / $schemaAdded value: +"https://json-schema.org/draft/2020-12/schema"search_shop_policies_and_faqs1 field changedInput schema / $schemaAdded value: +"https://json-schema.org/draft/2020-12/schema"update_cart1 field changedInput schema / $schemaAdded value: +"https://json-schema.org/draft/2020-12/schema"get_cartget_product_detailssearch_catalogsearch_shop_policies_and_faqsupdate_cartAgentic commerce gateway: discovery, search, checkout across Shopify/Woo/Odoo/PrestaShop.
Shopify product discovery and x402-paid offer verification for AI agents.
AI-agent product catalog: search, lookup & purchase routing over verified merchant data.
AI-powered commerce API for luxury skincare shopping. Enables AI agents to search products, browse collections, manage shopping carts, and generate checkout URLs for the Regenique Elegance Shopify store.
Add one secure layer between your agents and this server.
